System and method for pet-ct image artifact evaluation and correction

ABSTRACT

A method includes obtaining emission-tomography functional image data and a corresponding reconstructed anatomical image volume including at least one organ having natural motion; pre-determining a dedicated model for spatial mismatch correction of the at least one organ; performing initial image reconstruction of the emission-tomography functional image data to generate a reconstructed emission-tomography functional image volume utilizing attenuation correction based on the corresponding reconstructed anatomical image volume; and identifying relevant anatomical regions, within both image volumes, where functional image quality may be affected by the natural motion of the at least one organ. The method includes identifying and evaluating potential attenuation-correction image artifacts in the reconstructed emission-tomography functional image volume; estimating model parameters based on confirmed attenuation-correction image artifacts; correcting the corresponding reconstructed anatomical image volume to generate a corrected anatomical image volume; and reconstructing the emission-tomography functional image data utilizing attenuation correction based on the corrected anatomical image volume.

BACKGROUND

The subject matter disclosed herein relates to medical imaging and, moreparticularly, to medical image artifact correction.

Non-invasive imaging technologies allow images of the internalstructures or features of a patient/object to be obtained withoutperforming an invasive procedure on the patient/object. In particular,such non-invasive imaging technologies rely on various physicalprinciples (such as the differential transmission of X-rays through atarget volume, the reflection of acoustic waves within the volume, theparamagnetic properties of different tissues and materials within thevolume, the breakdown of targeted radionuclides within the body, and soforth) to acquire data and to construct images or otherwise representthe observed internal features of the patient/object.

In functional medical imaging modalities such as positron emissiontomography (PET) and single photon emission computed tomography (SPECT),attenuation correction is an important part of the image reconstructionprocess. Typically, the data for an attenuation correction algorithm isgenerated from an associated anatomical image scan with computedtomography (CT) or magnetic resonance imaging (MM). For achieving highquality functional images, the spatial matching or registration betweenthe two modalities needs to be accurate. Common sources of imagemisregistration are sporadic patient movement and natural respiratorymotion or cardiac organ motion. Although it would be always beneficialto achieve functional and anatomical image data acquired at the samepatient motion phase, this is usually difficult to accomplish with thetypical clinical protocol considerations. Therefore, attempts have beenmade to algorithmically correct this problem within an imagereconstruction framework.

Attenuation-correction mismatch image artifacts can affect the accuracyand reliability of clinical diagnostics since radiotracer uptake inlesions or other structured tissues may appear significantly too low ortoo high relative to the true values. For example, lesions in the upperliver region or lesions in the lower lung areas may significantly beaffected by respiratory motion leading to functional-anatomicalmismatch. In cardiac imaging, the left ventricle imaged uptake may beaffected by the myocardium expansion and contraction cycle. A relatedcommon problem is that imaged regions of the lower lung have strongactivity value suppression which may cause the physician to miss thetrue clinical findings.

Several different approaches have been attempted in trying to mitigatethe described artifact problem. In one known approach, in PET and SPECT,the reviewed functional images are typically the average along time ofthe acquired data during natural organ motion, or they are the result ofa selected reconstructed phase (i.e., “freeze” state) from a gatedacquisition (instrumental-based or data-driven). The correspondinganatomical images, like from CT, are typically acquired in anasynchronized breath-hold scan, or in an arbitrary short time frame froma natural-breathing scan. It is possible to select a specificreconstructed PET phase with probable best registration to the CTimages. However, in this approach, the PET image quality may besignificantly degraded, and the optimal registration to the CT is stillnot guaranteed. Additionally, known approaches of 3D image-based PET-CTregistration can help as well, but only if there is sufficientstructural similarity between the relevant functional and anatomicalimage structures. Unfortunately, such similarity is not alwaysguaranteed, and particularly large structural differences may exist insituations with severe artifact. Further, some other combinations andvariations of these approaches were investigated as well. In anypractical chosen solution, the overall computational time should also bean important consideration.

In addition, the described artifact problem may be worse (i.e.,attenuation mismatch artifacts are stronger) in PET systems lackingtime-of-flight (TOF) capabilities. In particular, these artifacts mayappear in total body, non-TOF PET systems with wide coincidenceacceptance angle, where the projection rays may frequently passparticularly high attenuation paths in the patient body.

BRIEF DESCRIPTION

A summary of certain embodiments disclosed herein is set forth below. Itshould be understood that these aspects are presented merely to providethe reader with a brief summary of these certain embodiments and thatthese aspects are not intended to limit the scope of this disclosure.Indeed, this disclosure may encompass a variety of aspects that may notbe set forth below.

In one embodiment, a computer-implemented method for automatic artifactevaluation and correction in medical imaging data is provided. Themethod includes obtaining, via a processor, emission-tomographyfunctional image data and a corresponding reconstructed anatomical imagevolume of a subject, the emission-tomography functional image data andthe corresponding reconstructed anatomical image volume including atleast one organ having natural motion. The method also includespre-determining, via the processor, a dedicated model for spatialmismatch correction of the at least one organ having natural motion. Themethod further includes performing, via the processor, initial imagereconstruction of the emission-tomography functional image data togenerate a reconstructed emission-tomography functional image volumeutilizing attenuation correction based on the correspondingreconstructed anatomical image volume. The method even further includesidentifying, via the processor, relevant anatomical regions, within thereconstructed emission-tomography functional image volume and thecorresponding reconstructed anatomical image volume, where functionalimage quality may be affected by the natural motion of the at least oneorgan. The method still further includes identifying and evaluating, viathe processor, potential attenuation-correction image artifacts in thereconstructed emission-tomography functional image volume that arerelated to functional-anatomical spatial mismatch. The method yetfurther includes estimating, via the processor, model parameters basedon confirmed attenuation-correction image artifacts, wherein the modelparameters represent the functional-anatomical spatial mismatch. Themethod further includes correcting, via the processor, the correspondingreconstructed anatomical image volume utilizing both the dedicated modeland the model parameters to generate a corrected anatomical imagevolume. The method still further includes reconstructing, via theprocessor, the emission-tomography functional image data utilizingattenuation correction based on the corrected anatomical image volume togenerate a corrected emission-tomography functional image volume.

In another embodiment, a system for automatic artifact evaluation andcorrection in medical imaging data is provided. The system includes amemory encoding processor-executable routines. The system also includesa processor configured to access the memory and to execute theprocessor-executable routines, wherein the routines, when executed bythe processor, cause the processor to perform actions. The actionsinclude obtaining emission-tomography functional image data and acorresponding reconstructed anatomical image volume of a subject, theemission-tomography functional image data and the correspondingreconstructed anatomical image volume including at least one organhaving natural motion. The actions also include pre-determining adedicated model for spatial mismatch correction of the at least oneorgan having natural motion. The actions further include performinginitial image reconstruction of the emission-tomography functional imagedata to generate a reconstructed emission-tomography functional imagevolume utilizing attenuation correction based on the correspondingreconstructed anatomical image volume. The actions even further includeidentifying relevant anatomical regions, within the reconstructedemission-tomography functional image volume and the correspondingreconstructed anatomical image volume, where functional image qualitymay be affected by the natural motion of the at least one organ. Theactions still further include identifying and evaluating potentialattenuation-correction image artifacts in the reconstructedemission-tomography functional image volume that are related tofunctional-anatomical spatial mismatch. The actions yet further includeestimating model parameters based on confirmed attenuation-correctionimage artifacts, wherein the model parameters represent thefunctional-anatomical spatial mismatch. The actions further includecorrecting the corresponding reconstructed anatomical image volumeutilizing both the dedicated model and the model parameters to generatea corrected anatomical image volume. The actions still further includereconstructing the emission-tomography functional image data utilizingattenuation correction based on the corrected anatomical image volume togenerate a corrected emission-tomography functional image volume.

In a further embodiment, a non-transitory computer-readable medium isprovided. The computer-readable medium includes processor-executablecode that when executed by a processor, causes the processor to performactions. The actions include obtaining emission-tomography functionalimage data and a corresponding reconstructed anatomical image volume ofa subject, the emission-tomography functional image data and thecorresponding reconstructed anatomical image volume including at leastone organ having natural motion. The actions also includepre-determining a dedicated model for spatial mismatch correction of theat least one organ having natural motion. The actions further includeperforming initial image reconstruction of the emission-tomographyfunctional image data to generate a reconstructed emission-tomographyfunctional image volume utilizing attenuation correction based on thecorresponding reconstructed anatomical image volume. The actions evenfurther include identifying relevant anatomical regions, within thereconstructed emission-tomography functional image volume and thecorresponding reconstructed anatomical image volume, where functionalimage quality may be affected by the natural motion of the at least oneorgan. The actions still further include identifying and evaluatingpotential attenuation-correction image artifacts in the reconstructedemission-tomography functional image volume that are related tofunctional-anatomical spatial mismatch. The actions yet further includeestimating model parameters based on confirmed attenuation-correctionimage artifacts, wherein the model parameters represent thefunctional-anatomical spatial mismatch. The actions further includecorrecting the corresponding reconstructed anatomical image volumeutilizing both the dedicated model and the model parameters to generatea corrected anatomical image volume. The actions still further includereconstructing the emission-tomography functional image data utilizingattenuation correction based on the corrected anatomical image volume togenerate a corrected emission-tomography functional image volume.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentdisclosure will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a diagrammatical representation of an embodiment of a PETimaging system, in accordance with aspects of the present disclosure;

FIG. 2 is a perspective view of a PET-CT imaging system having the PETimaging system of FIG. 1 , in accordance with aspects of the presentdisclosure;

FIG. 3 is a perspective view of a PET-MRI imaging system having the PETimaging system of FIG. 1 , in accordance with aspects of the presentdisclosure;

FIG. 4 is a flowchart of a method for automatic artifact evaluation andcorrection in medical imaging data, in accordance with aspects of thepresent disclosure;

FIG. 5 is a flowchart of a method for detecting and estimating modelparameters of attenuation-correction image artifacts, in accordance withaspects of the present disclosure;

FIG. 6 is a flowchart of a method for correcting an anatomical imagevolume, in accordance with aspects of the present disclosure;

FIG. 7 is a flowchart of a method for detecting attenuation-correctionimage artifacts and estimating model parameters of theattenuation-correction image artifacts (e.g., utilizing machine-learningtechniques or deep-learning techniques), in accordance with aspects ofthe present disclosure;

FIG. 8 is a flowchart of a method for generating training data formachine-learning- or deep-learning-based detection ofattenuation-correction image artifacts and estimation of modelparameters of the attenuation-correction image artifacts, in accordancewith aspects of the present disclosure;

FIG. 9 provides examples of images of a patient illustrating results ofautomatic artifact evaluation and correction; and

FIG. 10 provides examples of images of a patient illustratingapplication of automatic artifact evaluation and correction.

DETAILED DESCRIPTION

One or more specific embodiments will be described below. In an effortto provide a concise description of these embodiments, not all featuresof an actual implementation are described in the specification. Itshould be appreciated that in the development of any such actualimplementation, as in any engineering or design project, numerousimplementation-specific decisions must be made to achieve thedevelopers' specific goals, such as compliance with system-related andbusiness-related constraints, which may vary from one implementation toanother. Moreover, it should be appreciated that such a developmenteffort might be complex and time consuming, but would nevertheless be aroutine undertaking of design, fabrication, and manufacture for those ofordinary skill having the benefit of this disclosure.

When introducing elements of various embodiments of the present subjectmatter, the articles “a,” “an,” “the,” and “said” are intended to meanthat there are one or more of the elements. The terms “comprising,”“including,” and “having” are intended to be inclusive and mean thatthere may be additional elements other than the listed elements.Furthermore, any numerical examples in the following discussion areintended to be non-limiting, and thus additional numerical values,ranges, and percentages are within the scope of the disclosedembodiments.

As utilized herein, “functional medical imaging” relates to revealingphysiological activities within a certain tissue or organ by employingmedical image modalities (e.g., PET, SPECT, CT perfusion imaging,functional MM) that often utilize tracers or probes to reflect spatialdistribution of them within the body. As utilized herein, “anatomicalmedical imaging” or “structural medical imaging” relates to thevisualization and analysis of anatomical properties of a certain tissueor organ utilizing certain medical image modalities (e.g., CT,structural Mill, diffused-based MRI).

Machine learning techniques, whether deep learning networks or otherexperiential/observational learning system, can be used to locate anobject in an image, understand speech and convert speech into text, andimprove the relevance of search engine results, for example. Deeplearning is a subset of machine learning that uses a set of algorithmsto model high-level abstractions in data using a deep graph withmultiple processing layers including linear and non-lineartransformations. While many machine learning systems are seeded withinitial features and/or network weights to be modified through learningand updating of the machine learning network, a deep learning networktrains itself to identify “good” features for analysis. Using amultilayered architecture, machines employing deep learning techniquescan process raw data better than machines using conventional machinelearning techniques. Examining data for groups of highly correlatedvalues or distinctive themes is facilitated using different layers ofevaluation or abstraction.

Throughout the specification and claims, the following terms take themeanings explicitly associated herein, unless the context clearlydictates otherwise. The term “deep learning” is a machine learningtechnique that utilizes multiple data processing layers to recognizevarious structures in data sets and classify the data sets with highaccuracy. A deep learning network can be a training network (e.g., atraining network model or device) that learns patterns based on aplurality of inputs and outputs. A deep learning network can be adeployed network (e.g., a deployed network model or device) that isgenerated from the training network and provides an output in responseto an input.

The present disclosure provides systems and methods for automaticartifact evaluation and correction in medical imaging data. Inparticular, the described systems and methods take a different approachfrom those described above to address attenuation-correction mismatchimage artifacts (e.g., due to PET-CT respiratory mismatch). Thedisclosed systems and methods utilize a dedicated algorithm (e.g.,automatic artifact evaluation and correction algorithm) to evaluate theoriginally reconstructed PET image artifacts. The algorithm is based onaccurately identifying the relevant anatomical regions which may besusceptible to artifacts, and some other relevant organs or regions.Then, key characteristics of the morphological structures andintensities of the artifacts are calculated based on the PET imagevalues (e.g., for use with a modeled CT image deformation based onestimated respiratory motion pattern). In certain embodiments, this stepcan be assisted by machine-learning or deep-learning techniques. Fromthis evaluation, the spatial range of either “missing” or “over-presenceof” attenuating tissues is estimated. The deformation model of the CTimage volume is based on the knowledge that the mismatch is caused byspecific natural organ motion, typically periodic. Therefore, it ispossible to estimate the structural changes of the relevant organs andtheir vicinity on different phases along the motion cycle, if main keyparameters representing the difference relative to the original imagedphase are known. For example, the respiratory motion in the lower lungregion is caused by the diaphragm motion, which mainly causes the liverand spleen to stretch up or down against the adjacent volume and tissuesof the lungs. The diaphragm itself is a very thin muscle tissue. Thisexpansion or contraction is typically within predicted limits andmorphological constraints. In cardiac motion cycle, it is also possibleto estimate the expanded or contracted heart and myocardium shapes alongthe cycle, if the average phase position along that cycle is known. Thederived few key parameters from the image artifact evaluation enablesestimating the motion phase along the organ motion cycle, and enablesuse of the deformation model to modify the CT image volume accordingly.The modified CT is used to reconstruct corrected PET images with moreaccurate attenuation correction. The goal of the CT deformation processis to achieve an artificial anatomical volume which is much closer tothe underlying anatomy positions that correspond to the PET image volumethan what the original CT volume provides (even if the artificiallydeformed CT volume is not fully accurate by itself). The modified CTimages may typically be used only for the attenuation correction in thereconstruction and not directly for clinical review and diagnostics.

The disclosed systems and methods provide an approach that can beutilized in situations where severe artifacts exist and any spatialregistration algorithms between the PET and CT images cannot provide therequired result. In addition, the disclosed systems and methods providean approach that is particularly suited for large axial coverage non-TOFPET systems (e.g., total body PET systems). The disclosed embodimentscan be applied to correct similar artifacts in cardiac PET-CT and othermulti-modalities (e.g., SPECT-CT and PET-MM).

With the foregoing in mind and turning now to the drawings, FIG. 1depicts a PET or SPECT system 10 operating in accordance with certainaspects of the present disclosure. The PET or SPECT imaging system ofFIG. 1 may be utilized with a dual-modality imaging system such as aPET-CT imaging system described in FIG. 2 or a PET-MRI imaging systemdescribed in FIG. 3 .

Returning now to FIG. 1 , the depicted PET or SPECT system 10 includes adetector 12 (or detector array). The detector 12 of the PET or SPECTsystem 10 typically includes a number of detector modules or detectorassemblies (generally designated by reference numeral 14) arranged inone or more rings, as depicted in FIG. 1 , each detector assembly 14includes multiple detector units (e.g., 3 to 5 detector units or more).The depicted PET or SPECT system 10 also includes a PET scannercontroller 16, a controller 18, an operator workstation 20, and an imagedisplay workstation 22 (e.g., for displaying an image). In certainembodiments, the PET scanner controller 16, controller 18, operatorworkstation 20, and image display workstation 22 may be combined into asingle unit or device or fewer units or devices.

The PET scanner controller 16, which is coupled to the detector 12, maybe coupled to the controller 18 to enable the controller 18 to controloperation of the PET scanner controller 16. Alternatively, the PETscanner controller 16 may be coupled to the operator workstation 20which controls the operation of the PET scanner controller 16. Inoperation, the controller 18 and/or the workstation 20 controls thereal-time operation of the PET system or SPECT system 10. In certainembodiments the controller 18 and/or the workstation 20 may control thereal-time operation of another imaging modality (e.g., the CT imagingsystem in FIG. 2 ) to enable the simultaneous and/or separateacquisition of image data from the different imaging modalities. One ormore of the PET scanner controller 16, the controller 18, and/or theoperation workstation 20 may include a processor 24 and/or memory 26. Incertain embodiments, the PET or SPECT system 10 may include a separatememory 28. The detector 12, PET scanner controller 16, the controller18, and/or the operation workstation 20 may include detector acquisitioncircuitry for acquiring image data from the detector 12, imagereconstruction and processing circuitry for image processing, and/orcircuitry for regulating the temperature of the detector units of thedetector assemblies 14 (e.g., independently regulating the temperatureof each detector assembly 14). The circuitry may include speciallyprogrammed hardware, memory, and/or processors.

The processor 24 may include multiple microprocessors, one or more“general-purpose” microprocessors, one or more special-purposemicroprocessors, and/or one or more application specific integratedcircuits (ASICS), system-on-chip (SoC) device, or some other processorconfiguration. For example, the processor 24 may include one or morereduced instruction set (RISC) processors or complex instruction set(CISC) processors. The processor 24 may execute instructions to carryout the operation of the PET or SPECT system 10. These instructions maybe encoded in programs or code stored in a tangible non-transitorycomputer-readable medium (e.g., an optical disc, solid state device,chip, firmware, etc.) such as the memory 26, 28. In certain embodiments,the memory 26 may be wholly or partially removable from the controller16, 18.

By way of example, PET imaging is primarily used to measure metabolicactivities that occur in tissues and organs and, in particular, tolocalize aberrant metabolic activity. In PET imaging, the patient istypically injected with a solution that contains a radioactive tracer.The solution is distributed and absorbed throughout the body indifferent degrees, depending on the tracer employed and the functioningof the organs and tissues. For instance, tumors typically process moreglucose than a healthy tissue of the same type. Therefore, a glucosesolution containing a radioactive tracer may be disproportionatelymetabolized by a tumor, allowing the tumor to be located and visualizedby the radioactive emissions. In particular, the radioactive traceremits positrons that interact with and annihilate complementaryelectrons to generate pairs of gamma rays. In each annihilationreaction, two gamma rays traveling in opposite directions are emitted.In a PET imaging system 10, the pair of gamma rays are detected by thedetector array 12 configured to ascertain that two gamma rays detectedsufficiently close in time are generated by the same annihilationreaction. Due to the nature of the annihilation reaction, the detectionof such a pair of gamma rays may be used to determine the line ofresponse along which the gamma rays traveled before impacting thedetector, allowing localization of the annihilation event to that line.By detecting a number of such gamma ray pairs, and calculating thecorresponding lines traveled by these pairs, the concentration of theradioactive tracer in different parts of the body may be estimated and atumor, thereby, may be detected. Therefore, accurate detection andlocalization of the gamma rays forms a fundamental and foremostobjective of the PET system 10.

As mentioned above, the PET or SPECT system 10 may be incorporated intoa dual-modality imaging system such as the PET-CT imaging system 30 inFIG. 2 . Referring now to FIG. 2 , the PET-CT imaging system 30 includesthe PET system 10 and a CT system 32 positioned in fixed relationship toone another. The PET system 10 and CT system 32 are aligned to allow fortranslation of a patient (not shown) therethrough. In use, a patient ispositioned within a bore 34 of the PET-CT imaging system 30 to image aregion of interest of the patient as is known in the art.

The PET system 10 includes a gantry 36 that is configured to support afull ring annular detector array 12 thereon (e.g., including theplurality of detector assemblies 14 in FIG. 1 ). The detector array 12is positioned around the central opening/bore 34 and can be controlledto perform a normal “emission scan” in which positron annihilationevents are counted. To this end, the detectors 14 forming array 12generally generate intensity output signals corresponding to eachannihilation photon.

The CT system 32 includes a rotatable gantry 38 having an X-ray source40 thereon that projects a beam of X-rays toward a detector assembly 42on the opposite side of the gantry 38. The detector assembly 42 sensesthe projected X-rays that pass through a patient and measures theintensity of an impinging X-ray beam and hence the attenuated beam as itpasses through the patient. During a scan to acquire X-ray projectiondata, gantry 38 and the components mounted thereon rotate about a centerof rotation. In certain embodiments, the CT system 32 may be controlledby the controller 18 and/or operator workstation 20 described in FIG. 2. In certain embodiments, the PET system 10 and the CT system 32 mayshare a single gantry. Image data may be acquired simultaneously and/orseparately with the PET system 10 and the CT system 32.

As mentioned above, the PET or SPECT system 10 may be incorporated intoa dual-modality imaging system such as the PET-MM imaging system 50 inFIG. 3 . Referring now to FIG. 3 , the PET-MRI imaging system 50includes the PET system 10 and a MM system 52 positioned in fixedrelationship to one another. The PET system 10 and MRI system 52 arealigned to allow for translation of a patient (not shown) therethrough.In use, a patient is positioned within a bore 54 of the PET-CT imagingsystem 50 to image a region of interest of the patient as is known inthe art. Image data may be acquired simultaneously and/or separatelywith the PET system 10 and the MRI system 52.

The PET-MRI imaging system 50 that includes a superconducting magnetassembly 56 that includes a superconducting magnet 58. Thesuperconducting magnet 58 is formed from a plurality of magnetic coilssupported on a magnet coil support or coil former. In one embodiment,the superconducting magnet assembly 56 may also include a thermal shield60. A vessel 62 (also referred to as a cryostat) surrounds thesuperconducting magnet 58, and the thermal shield 60 surrounds thevessel 62. The vessel 62 is typically filled with liquid helium to coolthe coils of the superconducting magnet 58. A thermal insulation (notshown) may be provided surrounding the outer surface of the vessel 62.The imaging system 50 also includes a main gradient coil 64, and the RFcoil assembly 66 that is mounted radially inwardly from the maingradient coil 64. As described above, a radio frequency (RF) coilassembly 66 includes the PET detector assembly 12, an RF transmit coil68 and the RF shield 70. More specifically, the RF coil assembly 66includes a coil support structure that is used to mount the PET detectorassembly 12, the RF transmit coil 68, and the RF shield 70.

In operation, the RF coil assembly 66 enables the imaging system 50 toperform both MM and PET imaging concurrently because both the RFtransmit coil 68 and the PET detector assembly 12 are placed around apatient at the center of the bore of the imaging system 50. Moreover,the PET detector assembly 12 is shielded from the RF transmit coil 68using the RF shield 70 that is disposed between the RF transmit coil 68and the PET detector assembly 12. Mounting the PET detector assembly 12,the RF transmit coil 68 and the RF shield 70 on the coil supportstructure enables the RF coil assembly 66 to be fabricated to have anoutside diameter that enables the RF coil assembly 66 to be mountedinside the gradient coil 64. Moreover, mounting the PET detectorassembly 12, the RF transmit coil 68 and the RF shield 70 on the coilsupport structure enables the RF coil assembly 66 to have a relativelylarge inside diameter to enable the imaging system 50 to image largerpatients.

The imaging system 50 also generally includes a controller 72, a mainmagnetic field control 74, a gradient field control 76, a memory 78, adisplay device 80, a transmit-receive (T-R) switch 82, an RF transmitter84, and a receiver 86.

In operation, a body of an object, such as a patient (not shown), or aphantom to be imaged, is placed in the bore 54 on a suitable support,for example, a motorized table (not shown) or the cradle describedabove. The superconducting magnet 58 produces a uniform and static mainmagnetic field Bo across the bore 54. The strength of theelectromagnetic field in the bore 54 and correspondingly in the patient,is controlled by the controller 72 via the main magnetic field control74, which also controls a supply of energizing current to thesuperconducting magnet 58.

The main gradient coil 64, which may include one or more gradient coilelements, is provided so that a magnetic gradient can be imposed on themagnetic field BO in the bore 54 in any one or more of three orthogonaldirections x, y, and z. The main gradient coil 64 is energized by thegradient field control 76 and is also controlled by the controller 72.

The RF coil assembly 66 is arranged to transmit magnetic pulses and/oroptionally simultaneously detect MR signals from the patient, if receivecoil elements are also provided. The RF coil assembly 66 may beselectably interconnected to one of the RF transmitter 84 or receiver86, respectively, by the T-R switch 82. The RF transmitter 84 and T-Rswitch 82 are controlled by the controller 72 such that RF field pulsesor signals are generated by the RF transmitter 84 and selectivelyapplied to the patient for excitation of magnetic resonance in thepatient.

Following application of the RF pulses, the T-R switch 82 is againactuated to decouple the RF coil assembly 66 from the RF transmitter 84.The detected MR signals are in turn communicated to the controller 72.The controller 72 includes a processor 88 that controls the processingof the MR signals to produce signals representative of an image of thepatient. The processed signals representative of the image are alsotransmitted to the display device 80 to provide a visual display of theimage. Specifically, the MR signals fill or form a k-space that isFourier transformed to obtain a viewable image which may be viewed onthe display device 80.

The imaging system 50 also controls the operation of PET imaging.Accordingly, in various embodiments, the imaging system 50 may alsoinclude a coincidence processor 90 that is coupled between the detector12 and a PET scanner controller 92. The PET scanner controller 92 may becoupled to the controller 72 to enable the controller 72 to control theoperation of the PET scanner controller 92. Optionally, the PET scannercontroller 92 may be coupled to a workstation 94 which controls theoperation of the PET scanner controller 92. In operation, the exemplaryembodiment, the controller 72 and/or the workstation 94 controlsreal-time operation of the PET imaging portion of the imaging system 50.

More specifically, in operation, the signals output from the PETdetector assembly 12 are input to the coincidence processor 90. Invarious embodiments, the coincidence processor 90 assembles informationregarding each valid coincidence event into an event data packet thatindicates when the event took place and the position of a detector thatdetected the event. The valid events may then be conveyed to thecontroller 92 and utilized to reconstruct an image. Moreover, it shouldbe realized that images acquired from the MR imaging portion may beoverlaid onto images acquired from the PET imaging portion. Thecontroller 72 and/or the workstation 94 may a central processing unit(CPU) or computer 88 to operate various portions of the imaging system50. As used herein, the term “computer” may include any processor-basedor microprocessor-based system configured to execute the methodsdescribed herein. Accordingly, the controller 72 and/or the workstation94 may transmit and/or receive information from the PET detectorassembly 12 to both control the operation of the PET detector assembly12 and to receive information from the PET detector assembly 12.

The various embodiments and/or components, for example, the modules, orcomponents and controllers therein, such as of the imaging system 50,also may be implemented as part of one or more computers or processors.The computer or processor may include a computing device, an inputdevice, a display unit and an interface, for example, for accessing theInternet. The computer or processor may include a microprocessor. Themicroprocessor may be connected to a communication bus. The computer orprocessor may also include a memory. The memory may include RandomAccess Memory (RAM) and Read Only Memory (ROM). The computer orprocessor further may include a storage device, which may be a hard diskdrive or a removable storage drive such as an optical disk drive, solidstate disk drive (e.g., flash RAM), and the like. The storage device mayalso be other similar means for loading computer programs or otherinstructions into the computer or processor.

As used herein, the term “computer” or “module” may include anyprocessor-based or microprocessor-based system including systems usingmicrocontrollers, reduced instruction set computers (RISC), applicationspecific integrated circuits (ASICs), logic circuits, and any othercircuit or processor capable of executing the functions describedherein. The above examples are exemplary only, and are thus not intendedto limit in any way the definition and/or meaning of the term“computer”.

The computer or processor executes a set of instructions that are storedin one or more storage elements, in order to process input data. Thestorage elements may also store data or other information as desired orneeded. The storage element may be in the form of an information sourceor a physical memory element within a processing machine.

The set of instructions may include various commands that instruct thecomputer or processor as a processing machine to perform specificoperations such as the methods and processes of the various embodimentsof the disclosed subject matter. The set of instructions may be in theform of a software program, which may form part of a tangiblenon-transitory computer readable medium or media. The software may be invarious forms such as system software or application software. Further,the software may be in the form of a collection of separate programs ormodules, a program module within a larger program or a portion of aprogram module. The software also may include modular programming in theform of object-oriented programming. The processing of input data by theprocessing machine may be in response to operator commands, or inresponse to results of previous processing, or in response to a requestmade by another processing machine.

As used herein, the terms “software” and “firmware” may include anycomputer program stored in memory for execution by a computer, includingRAM memory, ROM memory, EPROM memory, EEPROM memory, and non-volatileRAM (NVRAM) memory. The above memory types are exemplary only, and arethus not limiting as to the types of memory usable for storage of acomputer program.

FIG. 4 is a flowchart of a method 96 (e.g., automatic artifactevaluation and correction algorithm) for automatic artifact evaluationand correction in medical imaging data. One or more steps of the method96 may be performed by processing circuitry of the imaging systemsdiscussed above or processing circuitry of a remote computing devicehaving processing circuitry and memory circuitry. One or more of thesteps of the method 96 may be performed simultaneously or in a differentorder from the order depicted in FIG. 4 .

The method 96 includes obtaining emission-tomography functional imagedata and a corresponding reconstructed anatomical image volume of asubject (e.g., patient), the emission-tomography functional image dataand the corresponding reconstructed anatomical image volume including atleast one organ having natural motion (block 98). In certainembodiments, this imaging data may be acquired for multiple organshaving natural motion. For example, the emission-tomography functionalimage data may be acquired PET image data of the subject with may bereconstructed with controlled parameters and assisting information(e.g., different attenuation maps). The corresponding reconstructedanatomical image volume may be a corresponding CT image volume of thesame patient. As an example, the PET and CT image data may include lowerregions of the lungs (or in a different example, the heart). Theseregions or organs have natural motion during the PET scan (e.g., whichtypically lasts a few minutes) or during the “freeze” state phase (e.g.,from a gated acquisition) within the organ motion cycle.

The method 96 also includes pre-determining a dedicated model forspatial mismatch correction of the at least one organ having naturalmotion (block 100). In certain embodiments, different dedicated modelsfor spatial mismatch correction may be pre-determined for differentorgans having natural motion. The pre-determined model is based on theassumption that if an image volume of the organ vicinity is given in aspecific (or arbitrary) cycle phase, just a few parameters (e.g., one ortwo parameters) are sufficient to predict how the organ vicinity shouldbe seen (at least roughly) on the images on a different cycle phase. Forexample, one such parameter may represent the mean of the organ edgebetween two different phases along the cycle. For example, in the lowerlung region, the organ edge could be the upper edge of the liver or thespleen. As another example, the organ edge in the heart could be leftedge of the left ventricle. The model determines how to artificiallytransform or modify the organ structures on the images based on thecalculated parameters. The estimation or determination of theseparameters is explained in greater detail below.

The method 96 further includes performing initial image reconstructionof the emission-tomography functional image data to generate areconstructed emission-tomography functional image volume utilizingattenuation correction based on the corresponding reconstructedanatomical image volume (block 102). This step is a standard initialreconstruction step.

The method 96 even further include identifying relevant anatomicalregions (e.g., in a sub-volume), within the reconstructedemission-tomography functional image volume and the correspondingreconstructed anatomical image volume, where functional image qualitymay be affected by the natural motion of the at least one organ (block104). In certain embodiments, relevant anatomical regions may beidentified for different organs with natural motion. For example,computer-vision and image processing techniques may be utilized toautomatically detect a sub-volume including the lower part of the lungsand the upper parts of the liver and spleen. The sub-volume may bedetected on the CT anatomical image volume (based on Hounsfield units(HU)) by first applying a rough lung detection and segmentationalgorithm. On the lung mask, the lower edges and the outer circumferenceof the lungs on this lower region may be automatically identified. Incertain embodiments, a whole body (e.g., soft tissue) volumetricsegmented mask may be utilized to identify the relevant anatomicalregion. The identified relevant anatomical region could be a volumetricslab with approximately a few centimeters width in the Z-direction (e.g.axial patient direction) and an ellipse shape in the XY (transverse)direction.

The method 96 still further includes identifying and evaluatingpotential attenuation-correction image artifacts in the reconstructedemission-tomography functional image volume that are related tofunctional-anatomical spatial mismatch (block 106). For example, in adetermined or identified relevant anatomical region, the algorithm(e.g., automatic artifact evaluation and correction algorithm) maysearch for voxels with functional image values (e.g., PET image valuessuch as standardized uptake values (SUV)) that are suspected to be toolow or too high relative to mean values (e.g., mean PET image values) inadjacent organs and tissues. For the identified voxels with too low ortoo high of functional image values, characteristics such as theintensities, shapes (e.g., morphology), size, and location of thesegroup of voxels may be evaluated.

The method 96 yet further includes estimating model parameters based onconfirmed attenuation-correction image artifacts, wherein the modelparameters represent the functional-anatomical spatial mismatch (block108). For example, the characteristics of the identified voxels may beutilized to determine the few model parameters. Examples of the modelparameters that are calculated are determined are a width in Z andcurvature change along XY. The model parameters are utilized indetermining the required CT deformation. Block 106 and 108 are describedin greater detail in FIG. 5 .

The method 96 further includes correcting the correspondingreconstructed anatomical image volume utilizing both the dedicated modeland the model parameters to generate a corrected anatomical image volume(block 110). For example, a new CT image volume may be generated wherethe liver, spleen, and their adjacent tissues are expanded toward thelungs, to imitate a situation similar to when the subject exhales partof the air in the lungs. Block 110 is described in greater detail inFIG. 6 .

The method 96 even further includes generating an attenuation map fromthe corrected anatomical image volume (block 112). For example, a newattenuation map may be generated based on the new CT image volume. Themethod 96 still further includes reconstructing the emission-tomographyfunctional image data utilizing attenuation correction based on thecorrected anatomical image volume to generate a correctedemission-tomography functional image volume (block 114). For example, acorrected PET image volume is reconstructed using the original PET dataand the new attenuation map.

The method 96 yet further includes causing the display of the correctedemission-tomography functional image volume on a display (e.g., userinterface) (block 116). For the corrected PET image volume may bevisualized together (e.g., fused) with the original diagnostic CT imagevolume. In certain embodiments, the corrected PET image volume may bevisualized together with the deformed CT image volume (e.g., correctedanatomical image volume). Although the deformed CT image volume may bebetter registered, the diagnostic accuracy of anatomical details may becompromised. Thus, in the latter case, a clear indication should beprovided to user to indicate that the CT image is deformed. In certainembodiments, the method 96 may be utilized in conjunction with anattenuation correction quality check (ACQC) user application. In such anapplication, the corrected PET images can be viewed (e.g., on a userinterface) in comparison with the original PET images (e.g., sometimesfused). In addition, the visualized detected image with an indication ofthe calculated required anatomical shifts. In certain embodiments, theinteractive application may enable a user to manually refine theseshifts (e.g., as a ±ΔZ) and apply a refined PET image reconstruction.

The method 96 includes a number of advantages. For example, the method96 does not need list-mode or gated PET reconstruction. Only thestandard original reconstruction and final reconstruction are neededwith an image processing/analysis algorithm in the middle between theoriginal and final reconstructions. With only two standardreconstructions, the method 96 provides a reasonable overallcomputational time. In addition, the method 96 is well suited forsituations where severe artifacts exits. In particular, situations withstrong regional activity suppression where any known spatialregistration algorithm between the PET and CT images cannot provide therequired result. Further, as noted above, the method 96 may beefficiently integrated with an ACQC user application.

FIG. 5 is a flowchart of a method 118 for detecting and estimating modelparameters of attenuation-correction image artifacts (e.g., blocks 106and 108 of the method 96 in FIG. 4 ). One or more steps of the method118 may be performed by processing circuitry of the imaging systemsdiscussed above or processing circuitry of a remote computing devicehaving processing circuitry and memory circuitry. One or more of thesteps of the method 118 may be performed simultaneously or in adifferent order from the order depicted in FIG. 5 .

The method 118 includes, for each determined or identified relevantanatomical region (e.g., sub-region or sub-volume), proximate organs andspecific sub-regions with the potential attenuation-correction imageartifacts (block 120). For example, the relevant sub-region forsearching for artifacts may be a volumetric slab which is placed on thelower lung region and that includes the upper regions of the liver andthe spleen. The proximate organs may be the whole lung volume (e.g., asa voxel mask) and the segmented liver volume (usually a roughsegmentation is sufficient).

The method 118 also includes calculating functional image values ofnormal-uptake regions (e.g., normal relevant to adjacent organ andtissues) in the identified proximate organs (and/or identifiedsub-regions) (block 122). For example, the calculated functional imagevalues may be the median of PET image values (e.g., SUV) on a voxel maskthat includes body soft tissues (e.g., located with the help of the CTHU values of the whole scanned patient). The calculated functional imagevalues may also be the median of the PET image values on the wholelungs. The calculated images may further be the median of the PET imagevalues on the whole liver. In certain embodiments, other statisticalfunctions (e.g., mean) other than the median may be utilized.

The method 118 further includes, on the reconstructedemission-tomography functional image volume, identifying voxels withrelatively low functional image values or relatively high functionalimage values compared to the calculated functional image values of thenormal-uptake regions based on pre-determined criteria (block 124). Forexample, the PET image values in the volumetric slab (e.g., from block120) may be compared relative to the normal uptake values obtained inblock 122. The pre-determined criteria may be logical criteria thatincludes differences, ratios, and/or parameter thresholds. In certainembodiments, deviation levels may also be calculated and utilized todetermine how much is too high and how much is too low.

The method 118 still further includes, on the reconstructedemission-tomography functional image volume, calculating characteristicsof any identified voxels with the relatively low functional image valuesor the relatively high functional image values relative to theidentified proximate organs (block 126). The groups of identified voxelscreate three-dimensional (3D) shapes in the image space. Theintensities, shape morphology, sizes, and their spatial locationsrelative to the adjacent body organs are important for evaluatingwhether the group of identified voxels are attenuation-correction imageartifacts. In addition, statistical characteristics (e.g., histogramanalysis, center-of-mass, etc.) of the spatial distribution of theidentified voxels may be relevant for the evaluation.

The method 118 yet further includes determining which of the potentialattenuation-correction image artifacts are confirmedattenuation-correction image artifacts based on characteristics of theidentified voxels (block 128). This determination is based on theinformation (e.g., identified voxels and characteristics of theidentified voxels) from blocks 124 and 126.

The method 118 even further includes determining characteristics of theconfirmed attenuation-correction image artifacts relevant forattenuation correction based on the pre-determined criteria (block 130).From the information from blocks 124 and 126, the final characteristicsof the actual or confirmed attenuation-correction image artifacts may bedetermined utilizing the pre-determined criteria mentioned above.

The method 118 further includes estimating or determining the modelparameters based on confirmed attenuation-correction image artifacts,wherein the model parameters represent the functional-anatomical spatialmismatch (block 132). This is equivalent to the block 108 of the method96 in FIG. 4 . Based on the final artifact characteristics, keyparameters (typically, only a few parameters) are determined to controlthe subsequent CT image volume deformation (e.g., used to correct forthe functional-anatomical spatial mismatch). For example, the parametersmay include the expansion or contraction width and/or curvatures ofconditional dilation (or conditional contraction) of a morphologicalprocess.

FIG. 6 is a flowchart of a method 134 for correcting an anatomical imagevolume (e.g., block 110 of the method 96 in FIG. 4 ). One or more stepsof the method 134 may be performed by processing circuitry of theimaging systems discussed above or processing circuitry of a remotecomputing device having processing circuitry and memory circuitry. Oneor more of the steps of the method 134 may be performed simultaneouslyor in a different order from the order depicted in FIG. 6 .

The method 134 includes determining, based on the characteristics of theidentified voxels of the confirmed attenuation-correction imageartifacts, global parameters related to how much anatomical shapes ofthe identified proximate organs are spatially altered to correct for thefunctional-anatomical spatial mismatch (block 136). Examples of spatialalteration include expansion, contraction, and/or translation. Forexample, for the diaphragm, the global parameters may include how muchto move the diaphragm up or down (e.g., relative to center or anotherreference point). As another example, for the diaphragm, the globalparameters may include how much to adjust the curvature.

The method 134 also includes determining, for the identified proximateorgans, structural deformation constraints based on the pre-determineddedicated model (block 138). The constraints may be related to spatiallimits, edge smoothness, shape continuation, and/or pre-determinedmorphology.

The method 134 further includes applying, based on the global parametersand the structural deformation constraints, one or more algorithms forstructural deformation, conditional dilation, and/or conditional erosionto artificially generate a new organ shape in the corrected anatomicalimage volume (block 140). In certain embodiments, if there are artifactsin different organs, a respective new organ shape may be determined forthe different organs with the artifacts.

In certain embodiments, subsequent to generating the new organ shape,the method 134 includes filling, for confirmed attenuation-correctionimage artifacts with the relatively low functional image values, fillingrespective new organ shapes with attenuation values from an adjacenthigh-attenuation organ (block 142). In certain embodiments, subsequentto generating the new organ shape, the method 134 includes filling, viathe processor, for confirmed attenuation-correction image artifacts withthe relatively high functional image values, filling the respective neworgan shapes with attenuation values from an adjacent low-attenuationorgan (block 144). In certain embodiments, the method 134 includesapplying a final smoothing or image processing shaping if needed (block146).

As an example for the application of the method 134, in the case ofrespiratory mismatch in the lower lung regions, the shape of the movingdiaphragm along respiratory cycle is generally predictable, and itdirectly affects the deformation of the adjacent organs (e.g., locatedabove and below). Therefore, the structural deformation constraints canbe determined with only a few model parameters. In the case of mismatchcaused by the cardiac cycle (e.g., in the heart region), the deformationalong the cycle can be predicted as well. Returning to the diaphragmexample, if the model parameters (e.g., derived from the artifactanalysis step) indicate that the diaphragm should move upward 15 mm onits center and only 2 mm on its circumference, all of the soft tissuesbelow the lungs will be expanded upward (and based on their CT HU)replacing CT lung tissue values, which results in imitating an exhaleprocess. The amount of expansion in each XY location will be related toits position relative to the diaphragm center. Although such artificialdeformation may not be anatomically accurate, it will give much betterattenuation correction in the PET image reconstruction process, leadingto new images without artifacts.

In certain embodiments, machine-learning techniques and deep-learningtechniques (e.g., utilizing one or more trained neural networks) may beutilized for detecting/identifying attenuation-correction imageartifacts and estimating model parameters of the attenuation-correctionimage artifacts (e.g., blocks 106 and 108 of the method 96 in FIG. 4 ).FIG. 7 is a flowchart of a method 148 for detectingattenuation-correction image artifacts and estimating model parametersof the attenuation-correction image artifacts (e.g., utilizingmachine-learning techniques or deep-learning techniques). For the method148, artifact evaluation in the diaphragm vicinity (i.e., the regions ofthe lower lungs and the upper liver and spleen) is utilized as anexample. One or more steps of the method 148 may be performed byprocessing circuitry of the imaging systems discussed above orprocessing circuitry of a remote computing device having processingcircuitry and memory circuitry. One or more of the steps of the method148 may be performed simultaneously or in a different order from theorder depicted in FIG. 7 .

The method 148 includes preforming coarse detection and segmentation ofthe lungs, the liver, and the whole-patient soft tissues based on thecorresponding reconstructed anatomical image volume (block 150). Themethod 148 also includes, with respect to the correspondingreconstructed anatomical image volume, calculating a statisticalfunction of the functional image values of the normal-uptake for eachsegmented organ or group of tissues (block 152). In certain embodiments,the statistical function may be the median value distribution. Incertain embodiments, a different percentile (or other criteria) otherthan the median can be pre-determined to be utilized to reflect thenormal tracer uptake in the segmented organ or group of tissues.

The method 148 further includes, based on the segmented organs and/ortissue groups, detecting and determining the sub-volume in which imageartifacts (e.g., attenuation-correction image artifacts) may appear andbe considered (block 154). In certain embodiments, the method 148 evenfurther includes, on the functional image sub-volume, identifying voxelswith relatively low or relatively high functional image values compartedto the normal uptake values (obtained in block 152) based onpre-determined criteria (block 156). The pre-determined criteria may bea combination of relative ratios and thresholds. In certain embodiments,the method 148 includes generating or identifying, on the functionalimage sub-volume, groups of voxels divided into high, medium, and lowartifact probabilities (e.g., with different discrete values (e.g.,functional image values) for each group (block 158).

In certain embodiments, the method 148 includes down-sampling thefunctional image sub-volume with the identified voxel groups to create amore efficient machine learning or deep-learning process that wouldrequire less training data (block 160). In certain embodiments, themethod 148 utilizes the down-sampled volumes with the identified voxelgroups or specific extracted image features (e.g., from the functionalimage sub-volume) as inputs to a machine-learning model or deep-learningmodel (e.g., having one or more trained neural networks) (block 162). Asan example, the target for each input may be the required averageexpansion or contraction in mm up or down (corresponding to patientbreathing and diaphragm motion model) and/or a mean diaphragm curvatureparameter (overall two-scalar output).

In the method 148, the voxel group of suspected artifacts areautomatically marked and scored (e.g., with a value within apre-determined range) for each identified suspected voxel within apre-determined sub-volume. The required few deformation model parametersmay be directly calculated from the marked group of voxels using acomplicated transform function that can be set and trained with variousmachine-learning techniques. For the training, a sufficiently large setof training data is required.

FIG. 8 is a flowchart of a method 164 for generating training data formachine-learning- or deep-learning-based detection ofattenuation-correction image artifacts and estimation of modelparameters of the attenuation-correction image artifacts (i.e., fortraining the machine-learning- or deep-learning model or algorithmutilized in the method 148 in FIG. 7 ). The method 164 is fullyautomatic and does not utilize any human-based image evaluation. One ormore steps of the method 164 may be performed by processing circuitry ofthe imaging systems discussed above or processing circuitry of a remotecomputing device having processing circuitry and memory circuitry. Oneor more of the steps of the method 164 may be performed simultaneouslyor in a different order from the order depicted in FIG. 8 .

The method 164 includes obtaining or collecting a large set of PET-CTcase data from cases from a plurality of subjects related to a relevantsystem type (imaging system type) and a relevant clinical protocol(block 166). The method 164 also includes, for each case: obtaining aninitial reconstructed functional image volume (or generating subsequentreconstructed functional image volume) (block 168), evaluating potentialartifacts (e.g., attenuation-correction image artifacts) in the initialreconstructed functional image volume (block 170), and rating a severityof each identified or confirmed artifact (block 172). The method 164further includes repeatedly modifying in pre-determined steps (of organexpansion or contraction) the reconstructed functional image volumebased on the pre-determined dedicated model (block 174). In eachmodifying step, the method 164 includes reconstructing the modifiedfunctional image volume (block 168) and repeating blocks 170 and 172.The method 164 even further includes, based on the artifact severityrating, finding the step of the anatomical data modification which givesthe minimal artifact severity (i.e., with least artifact severity)(block 176). The anatomical organ state of the step of the anatomicaldata modification with the minimal artifact severity is considered theoptimal functional-anatomical matching. The method 164 still furtherincludes saving, each intermediate reconstructed functional image volumeand the corresponding recorded anatomical modification parameters (asdetermined relative to the found optimal state) as a single input andtarget to a machine-learning model or deep-learning model trainingscheme (block 178).

FIG. 9 provides examples of images of a patient illustrating results ofautomatic artifact evaluation and correction utilizing the techniquesdescribed above (e.g., the method 96 in FIG. 4 ). The images wereobtained of the patient scanned consecutively on two different PET-CTsystems (i.e., a TOF PET system and a non-TOF PET system). The non-TOFPET system is configured for a whole body scan. Images 180, 182 wereobtained on the TOF PET system (i.e., a PET system with TOFreconstruction). Images 184, 186, 188, 190 were obtained on the non-TOFPET system (i.e., a PET system without TOF reconstruction). The images180, 184, 188 in row 192 are of a first coronal position. The images182, 186, 190 in row 194 are of a second different coronal position. Theimages 180, 182 from the TOF PET system lack attenuation-correctionartifacts. The images 184, and 186 from the non-TOF PET system, whenutilizing non-TOF PET reconstruction, include significant and severeattenuation-correction artifacts (e.g. due PET-CT respiratory mismatch)as indicated by the arrows 196. The algorithm disclosed in the method 96in FIG. 4 was directly applied to the original PET and CT data and newreconstructed image volumes were generated (i.e. images 188, 190). Theimages are free of attenuation-correction artifacts.

FIG. 10 provides examples of images of a patient illustratingapplication of automatic artifact evaluation and correction utilizingthe techniques described above (e.g., the method 96 in FIG. 4 ). Theimages were obtained from a scan of a patient on a non-TOF PET system.Images 198, 200, 202, 204 in row 206 are the original PET reconstructedimages for four different coronal slices. Attenuation-correction relatedimage artifacts are evaluated and scored by a dedicated algorithm. Thealgorithm was applied to the whole image volume which includes images198, 200, 202, 204. Images 208, 210, 212, 214 in row 216 illustratehighlighted regions 218, 220 detected as potential artifacts by thealgorithm in the PET data. The evaluation of these artifacts results inthe generation of key parameters for the CT deformation model. The modelis specific for the lower lung regions and the adjacent organs. The CTdeformation model creates an artificial CT image volume which is thenused to reconstruct a corrected PET image volume (with more accurateattenuation correction). Image 222 is a coronal slice of the CTAC imagevolume acquired from the patient (i.e., original CTAC image volume)prior to correction with the deformation model and the model parametersfrom the PET artifact evaluation. Image 224 is a coronal slice of thecorrected CTAC image volume (i.e., artificial image volume). Lines 226,228 indicate the changes to the liver and spleen shapes on the CT imagevolume (e.g., the shift upward as indicated by the arrows 230). Lighter(e.g., white) artifacts (as seen in the images 198, 200, 202, 204) inthe original PET data are typically corrected with a lift (e.g., shiftup), while darker (e.g., black) artifacts are typically lowered (e.g.,shifted down). The image-based algorithmic steps show in FIG. 10 (anddescribed in the method 96 in FIG. 4 ) are fully 3D.

Technical effects of the disclosed embodiments include providing systemsand methods for providing an approach that can be utilized in situationswhere severe artifacts exist and any spatial registration algorithmsbetween the PET and CT images cannot provide the required result. Inaddition, the disclosed systems and methods provide an approach that isparticularly suited for large axial coverage non-TOF PET systems (e.g.,total body PET systems). The disclosed embodiments can be applied tocorrect similar artifacts in cardiac PET-CT and other multi-modalities(e.g., SPECT-CT and PET-MRI).

The techniques presented and claimed herein are referenced and appliedto material objects and concrete examples of a practical nature thatdemonstrably improve the present technical field and, as such, are notabstract, intangible or purely theoretical. Further, if any claimsappended to the end of this specification contain one or more elementsdesignated as “means for [perform]ing [a function] . . . ” or “step for[perform]ing [a function] . . . ”, it is intended that such elements areto be interpreted under 35 U.S.C. 112(f). However, for any claimscontaining elements designated in any other manner, it is intended thatsuch elements are not to be interpreted under 35 U.S.C. 112(f).

This written description uses examples to disclose the present subjectmatter, including the best mode, and also to enable any person skilledin the art to practice the subject matter, including making and usingany devices or systems and performing any incorporated methods. Thepatentable scope of the subject matter is defined by the claims, and mayinclude other examples that occur to those skilled in the art. Suchother examples are intended to be within the scope of the claims if theyhave structural elements that do not differ from the literal language ofthe claims, or if they include equivalent structural elements withinsubstantial differences from the literal languages of the claims.

1. A computer-implemented method for automatic artifact evaluation andcorrection in medical imaging data, comprising: obtaining, via aprocessor, emission-tomography functional image data and a correspondingreconstructed anatomical image volume of a subject, theemission-tomography functional image data and the correspondingreconstructed anatomical image volume comprising at least one organhaving natural motion; pre-determining, via the processor, a dedicatedmodel for spatial mismatch correction of the at least one organ havingnatural motion; performing, via the processor, initial imagereconstruction of the emission-tomography functional image data togenerate a reconstructed emission-tomography functional image volumeutilizing attenuation correction based on the correspondingreconstructed anatomical image volume; identifying, via the processor,relevant anatomical regions, within the reconstructedemission-tomography functional image volume and the correspondingreconstructed anatomical image volume, where functional image qualitymay be affected by the natural motion of the at least one organ;identifying and evaluating, via the processor, potentialattenuation-correction image artifacts in the reconstructedemission-tomography functional image volume that are related tofunctional-anatomical spatial mismatch; estimating, via the processor,model parameters based on confirmed attenuation-correction imageartifacts, wherein the model parameters represent thefunctional-anatomical spatial mismatch; correcting, via the processor,the corresponding reconstructed anatomical image volume utilizing boththe dedicated model and the model parameters to generate a correctedanatomical image volume; and reconstructing, via the processor, theemission-tomography functional image data utilizing attenuationcorrection based on the corrected anatomical image volume to generate acorrected emission-tomography functional image volume.
 2. Thecomputer-implemented method of claim 1, the method further comprisinggenerating, via the processor, an attenuation map from the correctedanatomical image volume, wherein the attenuation map is utilized inreconstructing the emission-tomography functional image data to generatethe corrected emission-tomography functional image volume.
 3. Thecomputer-implemented method of claim 1, wherein identifying andevaluating, via the processor, the potential attenuation-correctionimage artifacts comprises: identifying, for each identified relevantbody region, proximate organs and specific sub-regions with thepotential attenuation-correction image artifacts; calculating functionalimage values of normal-uptake regions in the identified proximateorgans; on the reconstructed emission-tomography functional imagevolume, identifying voxels with relatively low functional image valuesor relatively high functional image values compared to the calculatedfunctional image values of the normal-uptake regions based onpre-determined criteria; on the reconstructed emission-tomographyfunctional image volume, calculating characteristics of any identifiedvoxels with the relatively low functional image values or the relativelyhigh functional image values relative to the identified proximateorgans; determining which of the potential attenuation-correction imageartifacts are confirmed attenuation-correction image artifacts based oncharacteristics of the identified voxels; and determiningcharacteristics of the confirmed attenuation-correction image artifactsrelevant for attenuation correction based on the pre-determinedcriteria.
 4. The computer-implemented method of claim 3, whereinestimating the model parameters based on confirmedattenuation-correction image artifacts comprises determining the modelparameters based on the characteristics.
 5. The computer-implementedmethod of claim 3, wherein correcting the corresponding reconstructedanatomical image volume comprises: determining, based on thecharacteristics of the identified voxels of the confirmedattenuation-correction image artifacts, global parameters related to howmuch anatomical shapes of the identified proximate organs are spatiallyaltered to correct for the functional-anatomical spatial mismatch;determining, for the identified proximate organs, structural deformationconstraints based on the dedicated model; and applying, based on theglobal parameters and the structural deformation constraints, one ormore algorithms for structural deformation, conditional dilation, orconditional erosion to artificially generate a new organ shape in thecorrected anatomical image volume.
 6. The computer-implemented method ofclaim 5, comprising, subsequent to generating the new organ shape:filling, via the processor, for confirmed attenuation-correction imageartifacts with the relatively low functional image values, fillingrespective new organ shapes with attenuation values from an adjacenthigh-attenuation organ; and filling, via the processor, for confirmedattenuation-correction image artifacts with the relatively highfunctional image values, filling the respective new organ shapes withattenuation values from an adjacent low-attenuation organ.
 7. Thecomputer-implemented method of claim 1, wherein identifying andevaluating the potential attenuation-correction image artifactscomprises utilizing a trained deep neural network to identify andevaluate the potential attenuation-correction image artifacts.
 8. Thecomputer-implemented method of claim 1, comprising causing, via theprocessor, display of the corrected emission-tomography functional imagevolume on a display.
 9. A system for automatic artifact evaluation andcorrection in medical imaging data, comprising: a memory encodingprocessor-executable routines; a processor configured to access thememory and to execute the processor-executable routines, wherein theroutines, when executed by the processor, cause the processor to: obtainemission-tomography functional image data and a correspondingreconstructed anatomical image volume of a subject, theemission-tomography functional image data and the correspondingreconstructed anatomical image volume comprising at least one organhaving natural motion; pre-determine a dedicated model for spatialmismatch correction of the at least one organ having natural motion;perform initial image reconstruction of the emission-tomographyfunctional image data to generate a reconstructed emission-tomographyfunctional image volume utilizing attenuation correction based on thecorresponding reconstructed anatomical image volume; identify relevantanatomical regions, within the reconstructed emission-tomographyfunctional image volume and the corresponding reconstructed anatomicalimage volume, where functional image quality may be affected by thenatural motion of the at least one organ; identify and evaluatepotential attenuation-correction image artifacts in the reconstructedemission-tomography functional image volume that are related tofunctional-anatomical spatial mismatch; estimate model parameters basedon confirmed attenuation-correction image artifacts, wherein the modelparameters represent the functional-anatomical spatial mismatch; correctthe corresponding reconstructed anatomical image volume utilizing boththe dedicated model and the model parameters to generate a correctedanatomical image volume; and reconstruct the emission-tomographyfunctional image data utilizing attenuation correction based on thecorrected anatomical image volume to generate a correctedemission-tomography functional image volume.
 10. The system of claim 9,wherein the routines, when executed by the processor, cause theprocessor to generate an attenuation map from the corrected anatomicalimage volume, wherein the attenuation map is utilized in reconstructingthe emission-tomography functional image data to generate the correctedemission-tomography functional image volume.
 11. The system of claim 9,wherein the routines, when executed by the processor, cause theprocessor, when identifying and evaluating the potentialattenuation-correction image artifacts, to: identify, for eachidentified relevant body region, proximate organs and specificsub-regions with the potential attenuation-correction image artifacts;calculate functional image values of normal-uptake regions in theidentified proximate organs; on the reconstructed emission-tomographyfunctional image volume, identify voxels with relatively low functionalimage values or relatively high functional image values compared to thecalculated functional image values of the normal-uptake regions based onpre-determined criteria; on the reconstructed emission-tomographyfunctional image volume, calculate characteristics of any identifiedvoxels with the relatively low functional image values or the relativelyhigh functional image values relative to the identified proximateorgans; determine which of the potential attenuation-correction imageartifacts are confirmed attenuation-correction image artifacts based onthe characteristics of the identified voxels; and determinecharacteristics of the confirmed attenuation-correction image artifactsrelevant for attenuation correction based on the pre-determinedcriteria.
 12. The system of claim 11, wherein the routines, whenexecuted by the processor, cause the processor, when estimating themodel parameters based on confirmed attenuation-correction imageartifacts, to determine the model parameters based on thecharacteristics.
 13. The system of claim 11, wherein the routines, whenexecuted by the processor, cause the processor, when correcting thecorresponding reconstructed anatomical image volume, to: determine,based on the characteristics of the identified voxels of the confirmedattenuation-correction image artifacts, global parameters related to howmuch anatomical shapes of the identified proximate organs are spatiallyaltered to correct for the functional-anatomical spatial mismatch;determine, for the identified proximate organs, structural deformationconstraints based on the dedicated model; and apply, based on the globalparameters and the structural deformation constraints, one or morealgorithms for structural deformation, conditional dilation, orconditional erosion to artificially generate a new organ shape in thecorrected anatomical image volume.
 14. The system of claim 13, whereinthe routines, when executed by the processor, cause the processor to:subsequent to generating the new organ shape: fill for confirmedattenuation-correction image artifacts with the relatively lowfunctional image values, filling respective new organ shapes withattenuation values from an adjacent high-attenuation organ; and fill forconfirmed attenuation-correction image artifacts with the relativelyhigh functional image values, filling the respective new organ shapeswith attenuation values from an adjacent low-attenuation organ.
 15. Anon-transitory computer-readable medium, the computer-readable mediumcomprising processor-executable code that when executed by a processor,causes the processor to: obtain emission-tomography functional imagedata and a corresponding reconstructed anatomical image volume of asubject, the emission-tomography functional image data and thecorresponding reconstructed anatomical image volume comprising at leastone organ having natural motion; pre-determine a dedicated model forspatial mismatch correction of the at least one organ having naturalmotion; perform initial image reconstruction of the emission-tomographyfunctional image data to generate a reconstructed emission-tomographyfunctional image volume utilizing attenuation correction based on thecorresponding reconstructed anatomical image volume; identify relevantanatomical regions, within the reconstructed emission-tomographyfunctional image volume and the corresponding reconstructed anatomicalimage volume, where functional image quality may be affected by thenatural motion of the at least one organ; identify and evaluatepotential attenuation-correction image artifacts in the reconstructedemission-tomography functional image volume that are related tofunctional-anatomical spatial mismatch; estimate model parameters basedon confirmed attenuation-correction image artifacts, wherein the modelparameters represent the functional-anatomical spatial mismatch; correctthe corresponding reconstructed anatomical image volume utilizing boththe dedicated model and the model parameters to generate a correctedanatomical image volume; and reconstruct the emission-tomographyfunctional image data utilizing attenuation correction based on thecorrected anatomical image volume to generate a correctedemission-tomography functional image volume.
 16. The non-transitorycomputer-readable medium of claim 15, wherein the processor-executablecode, when executed by the processor, cause the processor to generate anattenuation map from the corrected anatomical image volume, wherein theattenuation map is utilized in reconstructing the emission-tomographyfunctional image data to generate the corrected emission-tomographyfunctional image volume.
 17. The non-transitory computer-readable mediumof claim 15, wherein the processor-executable code, when executed by theprocessor, cause the processor, when identifying and evaluating thepotential attenuation-correction image artifacts, to: identify, for eachidentified relevant body region, proximate organs and specificsub-regions with the potential attenuation-correction image artifacts;calculate functional image values of normal-uptake regions in theidentified proximate organs; on the reconstructed emission-tomographyfunctional image volume, identify voxels with relatively low functionalimage values or relatively high functional image values compared to thecalculated functional image values of the normal-uptake regions based onpre-determined criteria; on the reconstructed emission-tomographyfunctional image volume, calculate characteristics of any identifiedvoxels with the relatively low functional image values or the relativelyhigh functional image values relative to the identified proximateorgans; determine which of the potential attenuation-correction imageartifacts are confirmed attenuation-correction image artifacts based onthe characteristics of the identified voxels; and determinecharacteristics of the confirmed attenuation-correction image artifactsrelevant for attenuation correction based on the pre-determinedcriteria.
 18. The non-transitory computer-readable medium of claim 17,wherein the processor-executable code, when executed by the processor,cause the processor, when estimating the model parameters based onconfirmed attenuation-correction image artifacts, to determine the modelparameters based on the characteristics.
 19. The non-transitorycomputer-readable medium of claim 17, wherein the processor-executablecode, when executed by the processor, cause the processor, whencorrecting the corresponding reconstructed anatomical image volume, to:determine, based on the characteristics of the identified voxels of theconfirmed attenuation-correction image artifacts, global parametersrelated to how much anatomical shapes of the identified proximate organsare spatially altered to correct for the functional-anatomical spatialmismatch; determine, for the identified proximate organs, structuraldeformation constraints based on the dedicated model; and apply, basedon the global parameters and the structural deformation constraints, oneor more algorithms for structural deformation, conditional dilation, orconditional erosion to artificially generate a new organ shape in thecorrected anatomical image volume.
 20. The non-transitorycomputer-readable medium of claim 19, wherein the processor-executablecode, when executed by the processor, cause the processor to: subsequentto generating the new organ shape: fill for confirmedattenuation-correction image artifacts with the relatively lowfunctional image values, filling respective new organ shapes withattenuation values from an adjacent high-attenuation organ; and fill forconfirmed attenuation-correction image artifacts with the relativelyhigh functional image values, filling the respective new organ shapeswith attenuation values from an adjacent low-attenuation organ.